Motor vehicle crashes are the single major occupational cause death for American workers. A great deal has been learned about prevention of motor vehicle crashes for the general public; much less is known about the underlying causes and effective preventive strategies for work-related motor vehicle crashes. Few studies have focused on non-fatal motor vehicle injuries in the workplace, and there is no comprehensive information on morbidity and costs of work-related motor vehicle crashes. This competing continuation represents a natural extension of our currently funded project Analysis of Capitated Payments in Workers' Compensation, (5 RO 1 OH03419-03) through which we have developed a database and prospective record linking system to capture comprehensive data on the number, magnitude, and economic impact of work-related injuries for the 29,000 employees of the City of Philadelphia. We now propose to expand that database through additional record linking of existing databases, in order to comprehensively analyze the nature and causes of occupational motor vehicle crashes involving City vehicles and to develop a risk factor model which accounts for morbidity and economic burden of these events. This proposal addresses the National Occupational Research Agenda (NORA) priority research areas of. Traumatic Injuries; Health Services Research; Intervention Effectiveness Research; Social and Economic Consequences of Worker Illness and Injury; and Surveillance Research Methods. To achieve these goals, we will pursue three highly focused Specific Aims: Create a comprehensive database of municipal employees and their work-related motor vehicle crashes suitable for risk factor analysis using existing data on municipal workers in the City of Philadelphia, including a broad range of information on all City drivers and vehicles which focuses on the morbidity and economic impacts of crashes and provides the foundation for an ongoing crash surveillance system. Develop predictive models to define the determinants of work-related motor vehicle crashes, by examining risk factors related to driver characteristics, vehicle factors, and crash factors, using retrospectively collected data on the City's workforce and vehicles. Test and validate predictive models of work-related motor vehicle crashes, by using prospectively collected data on the Citv's workforce and vehicles.